Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

Ahmad, Iftikhar; Ayub, Ahsan; Kano, Manabu; Cheema, Izzat Iqbal

doi:10.3390/pr8020243

Cited by 37 publications

(17 citation statements)

References 103 publications

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“…The volume of available data is exponentially increasing in the case of industrial processes. Furthermore, additional Internet of Things (IoT) tools are being invented and utilized every day [34]. Considering these facts, manufacturing processes are fundamentally changing.…”

Section: Multibroker-genetic-algorithm (Mb-ga) Scheduling Methodsmentioning

confidence: 99%

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

et al. 2020

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Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

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Section: Multibroker-genetic-algorithm (Mb-ga) Scheduling Methodsmentioning

confidence: 99%

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

et al. 2020

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“…Hence, soft sensors 2) have been extensively utilized for in-line water content monitoring and control as process analytical technology (PAT) tools. 3) Soft sensors are categorized into three types, i.e., whitebox, black-box, and gray-box models, as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Gray-box Soft Sensor for Water Content Monitoring in Fluidized Bed Granulation

2022

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Soft sensors are powerful tools for the implementation of process analytical technology (PAT). They are categorized into white-box (first-principle), black-box (statistical), and gray-box models. Gray-box models integrate white-box and black-box models to address each drawback, i.e., prediction accuracy and intuitiveness. Although they have been applied to various industrial processes, their applicability to water content monitoring in fluidized bed granulation has not been reported. In this study, we evaluated three types of gray-box models, i.e., parallel, serial, and combined gray-box models, in terms of prediction accuracy using real operating data on a commercial scale with two formulations. The gray-box models were constructed by integrating the heat and mass balance model (white-box model) and locally weighted partial least squares regression (LW-PLSR) model (black-box model). LW-PLSR was utilized to cope with collinearity and nonlinearity. In the serial gray-box models, LW-PLSR models adjusted the fitting parameters of the white-box model depending on the process parameters for each query. In the parallel gray-box or combined gray-box models, LW-PLSR models compensated for the output error of the white-box or serial gray-box models, respectively. The results demonstrated that all three types of gray-box models improved the prediction accuracy of the white-box models regardless of the formulation. Besides, we proposed the assessment method based on Hotelling's T 2 and Q residual for gray-box models using LW-PLSR, which contributes decision support to select gray-box or white-box model. The accurate and descriptive gray-box models are expected to enhance process understanding and precise quality control in fluidized bed granulation.

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“…Data-driven models, on the other hand, tend to be computationally efficient but require either too much data or have too little interpretability to solve many scientific problems . Due to the contrasting yet complementary strengths of data-driven and mechanistic approaches to model building, many authors have sought to combine these paradigms in ways that increase interpretability and lower data requirements. − Readers interested in a comparison of data-driven, mechanistic, and hybrid approaches to model building are encouraged to consult recent surveys. − …”

Section: Introductionmentioning

confidence: 99%

Two-Stage Approach to Parameter Estimation of Differential Equations Using Neural ODEs

Bradley

Boukouvala

2021

Ind. Eng. Chem. Res.

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Modeling physiochemical relationships using dynamic data is a common task in fields throughout science and engineering. A common step in developing generalizable, mechanistic models is to fit unmeasured parameters to measured data. However, fitting differential equation-based models can be computation-intensive and uncertain due to the presence of nonlinearity, noise, and sparsity in the data, which in turn causes convergence to local minima and divergence issues. This work proposes a merger of machine learning (ML) and mechanistic approaches by employing ML models as a means to fit nonlinear mechanistic ordinary differential equations (ODEs). Using a two-stage indirect approach, neural ODEs are used to estimate state derivatives, which are then used to estimate the parameters of a more interpretable mechanistic ODE model. In addition to its computational efficiency, the proposed method demonstrates the ability of neural ODEs to better estimate derivative information than interpolating methods based on algebraic data-driven models. Most notably, the proposed method is shown to yield accurate predictions even when little information is known about the parameters of the ODEs. The proposed parameter estimation approach is believed to be most advantageous when the ODE to be fit is strongly nonlinear with respect to its unknown parameters.

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Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

Cited by 37 publications

References 103 publications

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Gray-box Soft Sensor for Water Content Monitoring in Fluidized Bed Granulation

Two-Stage Approach to Parameter Estimation of Differential Equations Using Neural ODEs

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